Proceedings ArticleDOI
On rank aggregation for face recognition from videos
Himanshu Bhatt,Richa Singh,Mayank Vatsa +2 more
- pp 2993-2997
TLDR
A video based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images to facilitate matching two videos with large variations is presented.Abstract:
Face recognition from still face images suffers due to intrapersonal variations caused by pose, illumination, and expression that degrade the performance. On the other hand, videos provide abundant information that can be leveraged to compensate the limitations of still face images and enhance face recognition performance. This paper presents a video based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images. The video signature embeds diverse intra-personal and temporal variations across multiple frames, thus facilitates matching two videos with large variations. Two videos are matched by comparing their discriminative signatures using the Kendall tau similarity distance measure. Performance comparison with the benchmark results and a commercial face recognition system on the publicly available YouTube faces database show the efficacy of the proposed video based face recognition algorithm.read more
Citations
More filters
Journal ArticleDOI
On Recognizing Faces in Videos Using Clustering-Based Re-Ranking and Fusion
TL;DR: A video-based face recognition algorithm that computes a discriminative video signature as an ordered list of still face images from a large dictionary, which embeds diverse intra-personal variations and facilitates in matching two videos with large variations.
Proceedings ArticleDOI
Video-to-video face matching: Establishing a baseline for unconstrained face recognition
TL;DR: This work demonstrates that all three COTS matchers individually are superior to previously published face recognition results on the unconstrained YouTube Faces database and achieves a 20% improvement in accuracy over previously published results.
Proceedings ArticleDOI
On video based face recognition through adaptive sparse dictionary
TL;DR: This paper proposes a video-based face recognition method which improves upon the sparse representation framework with an intelligent and adaptive sparse dictionary that updates the current probe image into the training matrix based on continuously monitoring the probe video through a novel confidence criterion and a Bayesian inference scheme.
Patent
System for video based face recognition using an adaptive dictionary
TL;DR: In this paper, a dictionary including a target collection defined by images that are known with a defined level of certainty to include a subject and an imposter collection defined of images of individuals other than the subject is used.
Proceedings ArticleDOI
An approach to improvise recognition rate from occluded and pose variant faces
A. Vijayalakshmi,Pethuru Raj +1 more
TL;DR: A model that can increase the recognition rate with faces of different pose and faces subjected to occlusion is proposed and the technique of in-painting to restore the occluded face in a frame of video is introduced.
References
More filters
Journal ArticleDOI
Multi-PIE
TL;DR: This paper introduces the database, describes the recording procedure, and presents results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
Proceedings Article
Multi-PIE
TL;DR: The CMU Multi-PIE database as mentioned in this paper contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions, with a limited number of subjects, a single recording session and only few expressions captured.
Proceedings ArticleDOI
Face tracking and recognition with visual constraints in real-world videos
TL;DR: This work addresses the problem of tracking and recognizing faces in real-world, noisy videos using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting and introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework.
Proceedings ArticleDOI
Face recognition with image sets using manifold density divergence
TL;DR: A flexible, semi-parametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds is proposed, which leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds.
Book ChapterDOI
Face Recognition from Long-Term Observations
TL;DR: This work addresses the problem of face recognition from a large set of images obtained over time - a task arising in many surveillance and authentication applications and proposes an information-theoretic algorithm that classifies sets of images using the relative entropy between the estimated density of the input set and that of stored collections of images for each class.